Amplitude of derivatives approximated by continuous wavelet transform











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I do have a question regarding the meaning of the amplitude of an approximated derivative derived from a continuous wavelet transform. From what is known, applying the CWT (Haar wavelet) to a signal results in an approximation of the first derivative. Applying it several times result in higher order derivatives.



Since the amplitude of the resulting derivative is altered by the scale factor, the real amplitude of the derivative is not known.



My goal is to calculate the highest value of a noisy signal's first derivative. However, the noise is hurting a lot. I found the Savitzky Golay algorithm that should work out for me.



However, I was wondering if there is any way to get the real amplitude of a derivative derived from a CWT.



Ideas are welcome? Thanks






signal-processing wavelets







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    up vote
    0
    down vote

    favorite












    I do have a question regarding the meaning of the amplitude of an approximated derivative derived from a continuous wavelet transform. From what is known, applying the CWT (Haar wavelet) to a signal results in an approximation of the first derivative. Applying it several times result in higher order derivatives.



    Since the amplitude of the resulting derivative is altered by the scale factor, the real amplitude of the derivative is not known.



    My goal is to calculate the highest value of a noisy signal's first derivative. However, the noise is hurting a lot. I found the Savitzky Golay algorithm that should work out for me.



    However, I was wondering if there is any way to get the real amplitude of a derivative derived from a CWT.



    Ideas are welcome? Thanks






    signal-processing wavelets







    share|cite|improve this question


























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I do have a question regarding the meaning of the amplitude of an approximated derivative derived from a continuous wavelet transform. From what is known, applying the CWT (Haar wavelet) to a signal results in an approximation of the first derivative. Applying it several times result in higher order derivatives.



      Since the amplitude of the resulting derivative is altered by the scale factor, the real amplitude of the derivative is not known.



      My goal is to calculate the highest value of a noisy signal's first derivative. However, the noise is hurting a lot. I found the Savitzky Golay algorithm that should work out for me.



      However, I was wondering if there is any way to get the real amplitude of a derivative derived from a CWT.



      Ideas are welcome? Thanks






      signal-processing wavelets







      share|cite|improve this question















      I do have a question regarding the meaning of the amplitude of an approximated derivative derived from a continuous wavelet transform. From what is known, applying the CWT (Haar wavelet) to a signal results in an approximation of the first derivative. Applying it several times result in higher order derivatives.



      Since the amplitude of the resulting derivative is altered by the scale factor, the real amplitude of the derivative is not known.



      My goal is to calculate the highest value of a noisy signal's first derivative. However, the noise is hurting a lot. I found the Savitzky Golay algorithm that should work out for me.



      However, I was wondering if there is any way to get the real amplitude of a derivative derived from a CWT.



      Ideas are welcome? Thanks






      signal-processing wavelets




      signal-processing wavelets






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      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited 15 hours ago









      Henno Brandsma

      100k344107




      100k344107










      asked Nov 6 at 10:32









      user2799180

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